The Use of Computational Modeling to Link Sensory Processing with Behavior in Drosophila

Clemens, Jan; Murthy, Mala

doi:10.1007/978-3-319-57363-2_9

Cited by 3 publications

(2 citation statements)

References 71 publications

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“…Reverse correlation yields kernels that describe how a behavior is tuned to a stimulus. Kernels are particularly powerful in the context of the linear non-linear (LN) model, a simple and ubiquitous model in neuroscience that can be used to predict a neuron’s or animal’s response to stimulus ( Ringach and Shapley, 2004 ; Schwartz et al, 2006 ; Coen et al, 2014 ; Gepner et al, 2015 ; Hernandez-Nunez et al, 2015 ; Calhoun and Murthy, 2017 ; Clemens and Murthy, 2017 ). See in particular ( Gepner et al, 2015 ).…”

Section: Resultsmentioning

confidence: 99%

Temporal processing and context dependency in Caenorhabditis elegans response to mechanosensation

Liu

Sharma

Shaevitz

et al. 2018

eLife

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A quantitative understanding of how sensory signals are transformed into motor outputs places useful constraints on brain function and helps to reveal the brain’s underlying computations. We investigate how the nematode Caenorhabditis elegans responds to time-varying mechanosensory signals using a high-throughput optogenetic assay and automated behavior quantification. We find that the behavioral response is tuned to temporal properties of mechanosensory signals, such as their integral and derivative, that extend over many seconds. Mechanosensory signals, even in the same neurons, can be tailored to elicit different behavioral responses. Moreover, we find that the animal’s response also depends on its behavioral context. Most dramatically, the animal ignores all tested mechanosensory stimuli during turns. Finally, we present a linear-nonlinear model that predicts the animal’s behavioral response to stimulus.

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Section: Resultsmentioning

confidence: 99%

Temporal processing and context dependency in Caenorhabditis elegans response to mechanosensation

Liu

Sharma

Shaevitz

et al. 2018

eLife

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“…These species are noteworthy for the wealth of experimental techniques available for investigating detailed neural circuits underlying sensorimotor behaviour (Gahtan and Baier, 2004;Olsen and Wilson, 2008;Orger, 2016;Stowers et al, 2017). Thus, principles from our model could be used to guide research questions with these animal models (Clemens and Murthy, 2017) to investigate cellular and circuit mechanisms of prediction and confidence estimation for general motor coordination.…”

Section: Prediction and Confidence Estimation In General Motor Coordi...mentioning

confidence: 99%

Confidence in predicted position error explains saccadic decisions during pursuit

Coutinho

Lefèvre

Blohm

2021

Journal of Neurophysiology

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A fundamental problem in motor control is the coordination of complementary movement types to achieve a common goal. As a common example, humans view moving objects through coordinated pursuit and saccadic eye movements. Pursuit is initiated and continuously controlled by retinal image velocity. During pursuit, eye position may lag behind the target. This can be compensated by the discrete execution of a catch-up saccade. The decision to trigger a saccade is influenced by both position and velocity errors and the timing of saccades can be highly variable. The observed distributions of saccade frequency and trigger time remain poorly understood and this decision process remains imprecisely quantified. Here we propose a predictive, probabilistic model explaining the decision to trigger saccades during pursuit to foveate moving targets. In this model, expected position error and its associated uncertainty are predicted through Bayesian inference across noisy, delayed sensory observations (Kalman filtering). This probabilistic prediction is used to estimate the confidence that a saccade is needed (quantified through log-probability ratio), triggering a saccade upon accumulating to a fixed threshold. The model qualitatively explains behavioural observations on the frequency and trigger time distributions of saccades during pursuit over a range of target motion trajectories. Furthermore, this model makes novel predictions that saccade decisions are highly sensitive to uncertainty for small predicted position errors, but this influence diminishes as the magnitude of predicted position error increases. We suggest that this predictive, confidence-based decision making strategy represents a fundamental principle for the probabilistic neural control of coordinated movements.

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Confidence in predicted position error explains saccadic decisions during pursuit

Coutinho

Lefèvre

Blohm

2018

Preprint

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10A fundamental problem in motor control is the coordination of complementary movement types to 11 achieve a common goal. As a common example, humans view moving objects through coordinated 12 pursuit and saccadic eye movements. Pursuit is initiated and continuously controlled by retinal image 13 velocity. During pursuit, eye position may lag behind the target. This can be compensated by the discrete 14 execution of a catch-up saccade. The decision to trigger a saccade is influenced by both position and 15 velocity errors and the timing of saccades can be highly variable. The observed distributions of saccade 16 occurrence and trigger time remain poorly understood and this decision process remains imprecisely 17 quantified. Here we propose a predictive, probabilistic model explaining the decision to trigger saccades 18 during pursuit to foveate moving targets. In this model, expected position error and its associated 19 uncertainty are predicted through Bayesian inference across noisy, delayed sensory observations 20 (Kalman filtering). This probabilistic prediction is used to estimate the confidence that a saccade is 21 needed (quantified through log-probability ratio), triggering a saccade upon accumulating to a fixed 22 threshold. The model qualitatively explains behavioural observations on the probability of occurrence and 23 trigger time distributions of saccades during pursuit over a range of target motion trajectories.24 Furthermore, this model makes novel predictions about the influence of sensory uncertainty and target 25 motion parameters on saccade decisions. We suggest that this predictive, confidence-based decision 26 making strategy represents a fundamental principle for the probabilistic neural control of coordinated 27 movements. 28 New & Noteworthy 29This is the first stochastic dynamical systems model of pursuit-saccade coordination accounting 30 for noise and delays in the sensorimotor system. The model uses Bayesian inference to predictively 31 estimate visual motion, triggering saccades when confidence in predicted position error accumulates to a 32 threshold. This model explains saccade probability and trigger time distributions across target trajectories 33 and makes novel predictions about the influence of sensory uncertainty in saccade decisions during 34 pursuit.The coordination between continuously controlled and discretely triggered movements to achieve 37 a common goal remains a fundamental problem in neuroscience. This coordinated motor control is 38 exemplified in the pursuit and saccadic eye movements that humans perform when tracking moving 39 objects. Pursuit eye movements are continuously controlled to minimize the relative motion of a visual 40 image on the retina (Keller and Heinen, 1991; Lisberger, 2015). Due to noise and delays prevalent in 41 sensorimotor systems (Faisal et al., 2008; van Beers et al., 2002), pursuit trajectory may deviate from and 42 lag behind the true target trajectory (Osborne et al., 2005). Furthermore, pursuit gain in humans is 43 typically less than ...

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The Use of Computational Modeling to Link Sensory Processing with Behavior in Drosophila

Cited by 3 publications

References 71 publications

Temporal processing and context dependency in Caenorhabditis elegans response to mechanosensation

Temporal processing and context dependency in Caenorhabditis elegans response to mechanosensation

Confidence in predicted position error explains saccadic decisions during pursuit

Confidence in predicted position error explains saccadic decisions during pursuit

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